Predicting Audit Quality Based on Factors in the Fraud Hexagon
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Abstract
Background. An audit of financial statements is needed to ensure accountability and transparency, especially in public companies. One of the actions that undermines transparency in a report is the deliberate manipulation of financial statements by management.
Aim. This study aims to analyse and predict audit quality based on factors in the Fraud Hexagon. The results are expected to make a theoretical contribution to the development of agency theory-based audit literature and a practical contribution to strengthening the role of auditors in fraud prevention and disclosure.
Methods. A causal-comparative type of research, which utilised a quantitative approach, was used in this study. Using a logistic regression model, this study aims to evaluate how the components of the Fraud Hexagon impact audit quality. The population in this study is all non-financial BUMN operating in Indonesia. The sample selection was carried out using a purposive sampling technique based on specific criteria set by the researcher. This study uses secondary data on annual and financial reports of BUMN companies listed on the Indonesia Stock Exchange for 2015 to 2022.”
Result. The factors in the fraud hexagon used in the model simultaneously significantly affect audit quality. Based on the logistic regression model used, the probability of high audit quality under certain conditions is 18.96%.
Conclusion. Based on the test results obtained that: (1) The factors in hexagon fraud used in the model simultaneously have a significant effect on audit quality (2) Based on the classification results, the logistic regression model has an accuracy rate of 68.5%, which means that out of 184 observations, 126 observations were successfully classified correctly by the model (3) Factors in hexagon fraud can explain variations in the dependent variable, namely audit quality, by 24.5%. (4) Based on the logistic regression model used, it is obtained that the probability of high audit quality under certain conditions is 18.96%.
Implementation. This study's results can be mapped using logistic regression to identify the main factors in the Fraud Hexagon that significantly affect audit quality
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